ML for approximating quantum physics calculations has been extensively studied in the literature [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}. These studies have been either on relatively small-scale datasets (in the order of 100K structures), small molecule size (10–30 atoms), or equilibrium structures (3D structures with all-zero atomic forces). In contrast, our focus is on a large-scale dataset (in the order of 100M structures), larger molecule size (20–200 atoms), and non-equilibrium structures (3D structures with non-zero atomic forces), making the model scalability and expressiveness especially important.
